A new parameter estimation method for flying object based on bearing measurements

In this paper, to overcome the limitation of the real-time state tracking of PLE, an algorithm is presented that recursively computers the maximum likehood (ML) estimates of a flying object position and velocity. By combining a priori ML estimate of the flying object state vector and its error covariance matrix with bearing measurements, updated estimates are obtained, localization and tracking for flying object is accomplished fastly. Experiment results prove it a fast, stable, effective algorithm and a real time processing mode in tracking system.